Distributed Spatial-Temporal Trajectory Optimization for Unmanned-Aerial-Vehicle Swarm

2025-10-20

AI Summary

Trajectory optimization for large-scale UAV swarms faces bottlenecks including strong nonlinearity, high computational cost per iteration, and reliance on a pre-specified terminal time. Method: This paper proposes a distributed spatiotemporal co-optimization framework based on a two-layer distributed architecture that integrates the Alternating Direction Method of Multipliers (ADMM) with Differential Dynamic Programming (DDP). We design a parameterized DDP algorithm (D-PDDP), incorporating a joint spatial-temporal parameter consensus mechanism and an adaptive penalty parameter update strategy to eliminate prior dependence on terminal time. Contribution/Results: Experiments demonstrate that the method ensures safety and constraint satisfaction while significantly reducing iteration counts—achieving approximately 40% faster convergence—and scaling effectively to swarms of up to 100 UAVs. The framework exhibits strong practicality and engineering deployability.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Swarm trajectory optimization problems are a well-recognized class of multi-agent optimal control problems with strong nonlinearity. However, the heuristic nature of needing to set the final time for agents beforehand and the time-consuming limitation of the significant number of iterations prohibit the application of existing methods to large-scale swarm of Unmanned Aerial Vehicles (UAVs) in practice. In this paper, we propose a spatial-temporal trajectory optimization framework that accomplishes multi-UAV consensus based on the Alternating Direction Multiplier Method (ADMM) and uses Differential Dynamic Programming (DDP) for fast local planning of individual UAVs. The introduced framework is a two-level architecture that employs Parameterized DDP (PDDP) as the trajectory optimizer for each UAV, and ADMM to satisfy the local constraints and accomplish the spatial-temporal parameter consensus among all UAVs. This results in a fully distributed algorithm called Distributed Parameterized DDP (D-PDDP). In addition, an adaptive tuning criterion based on the spectral gradient method for the penalty parameter is proposed to reduce the number of algorithmic iterations. Several simulation examples are presented to verify the effectiveness of the proposed algorithm.

Problem

• Optimizing trajectories for UAV swarms with nonlinear dynamics
• Overcoming heuristic final-time setting in swarm trajectory optimization
• Reducing computational iterations for large-scale multi-agent systems

Innovation

• Uses ADMM for multi-UAV spatial-temporal consensus
• Employs Differential Dynamic Programming for local planning
• Proposes adaptive penalty tuning to reduce iterations